The Research Domain Criteria (RDoC) project was initiated by NIMH to provide a new approach to the assessment of mental disorders that may better suit the research mission of the NIMH. RDoC is based on the premise that mental illnesses are disorders of brain circuits, which consist of regions and pathways that work together to accomplish particular tasks, and if altered can result in psychopathology. We are especially interested in the impact of childhood maltreatment, as maltreatment and household dysfunction are the leading preventable causes of mental illness. Our primary focus is to delineate critical and sensitive periods in brain development when early life stress may alter RDoC-associated neurocircuits. Further, we have also argued that maltreated and non-maltreated individuals with the same primary psychiatric diagnosis are clinically and neurobiologically distinct. What this means is that brain imaging findings, such as reduced hippocampal volume or heightened amygdala response, may be seen in depressed individuals with histories of maltreatment but not in depressed individuals who were not maltreated. Hence, there is also a pressing need to know whether RDoC-associated neurocircuit abnormalities associated with symptoms of anxiety or anhedonia apply to all subjects or are specific to those with histories of maltreatment. Our aim is to test these hypotheses through secondary analyses of a data set that consists of MRI scans from 340 unmedicated individuals 18-25 years of age. These subjects were recruited from the community and have completed the Maltreatment and Abuse Chronology of Exposure scale that provides retrospective data on severity of exposure to ten types of maltreatment across each year of childhood. More than half of the total sample suffered from depression or anxiety. Two circuits will be examined. One circuit plays an important role in detecting and responding to threats and helps determine our fight of flight response. The other circuit is involved in predicting and evaluating the cost/benefits of rewards in order to determine how hard to work for them. Conventional statistical methods will be used to identify regions and pathways that are susceptible to early life stress, and data mining techniques will then be used to delineate the type and time of maltreatment that had the greatest effect on these structures. This information is important as it will enable us to understand what types of exposure place children at greatest risk for anxiety and depression and their associated neurobiological mechanisms. Further, this information may also reveal times when treatments may be most beneficial.